Construction of Emotional Evolution Map of Online Texts on Medical Student Burnout Integrating BERT and LDA Topic Models
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Abstract
Large-scale online medical communities provide continuous textual signals that reflect the psychological states of medical students; however, conventional survey-based approaches have limited capability for tracking the temporal evolution of burnout-related emotions. To address this issue, a hybrid analytical framework integrating BioBERT-based sentiment recognition and latent topic discovery is developed for dynamic emotional evolution modeling. Burnoutrelated texts collected from online medical forums are first transformed into contextual semantic representations through a domain-adapted BioBERT model, enabling fine-grained identification of positive emotion, negative emotion, emotional exhaustion, and depersonalization states. Subsequently, an LDA-based topic mining module is employed to extract latent stress-related themes and quantify topic distributions. By fusing sentiment trajectories with topic intensity information in the temporal domain, a multidimensional emotional evolution map is constructed to characterize the dynamic interactions between emotional states and discussion topics. Experimental results on 137,430 medical-student text records demonstrate that the proposed framework achieves 92.7% classification accuracy with a macro-F1 score of 0.904, while the optimized topic model reaches a coherence score of 0.632. Temporal analysis reveals distinct evolution patterns across internship stress, academic anxiety, career identity, and institutional support topics. The proposed framework provides an effective data-driven methodology for large-scale sentiment monitoring, temporal topic analysis, and dynamic behavioral-state assessment in complex online information environments.
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